Contents

1 RAVs best represents your dataset

The validation provides a quantitative representation of the relevance between your dataset and RAVs. Below shows the top 6 validated RAVs and the complete result is saved as {input_name}_validate.csv.

##              score PC          sw cl_size cl_num
## RAV1076 0.15262099  2 -0.04447124      10   1076
## RAV725  0.24823617  2  0.09412734      20    725
## RAV884  0.22061781  2  0.15328698       6    884
## RAV1994 0.09894967  4 -0.03493900       3   1994

1.1 Heatmap Table

heatmapTable takes validation results as its input and displays them into a two panel table: the top panel shows the average silhouette width (avg.sw) and the bottom panel displays the validation score.

heatmapTable can display different subsets of the validation output. For example, if you specify scoreCutoff, any validation result above that score will be shown. If you specify the number (n) of top validation results through num.out, the output will be a n-columned heatmap table. You can also use the average silhouette width (swCutoff), the size of cluster (clsizecutoff), one of the top 8 PCs from the dataset (whichPC).

Here, we print out top 3 validated RAVs with average silhouette width above 0.

1.2 Interactive Graph

Under the default condition, plotValidate plots validation results of all non single-element RAVs in one graph, where x-axis represents average silhouette width of the RAVs (a quality control measure of RAVs) and y-axis represents validation score. We recommend users to focus on RAVs with higher validation score and use average silhouette width as a secondary criteria.

Note that interactive = TRUE will result in a zoomable, interactive plot that included tooltips, which is saved as {input_name}_validate_plot.html file.

You can hover each data point for more information:

  • sw : the average silhouette width of the cluster
  • score : the top validation score between 8 PCs of the dataset and RAVs
  • cl_size : the size of RAVs, represented by the dot size
  • cl_num : the RAV number. You need this index to find more information about the RAV.
  • PC : test dataset’s PC number that validates the given RAV. Because we used top 8 PCs of the test dataset for validation, there are 8 categories.

If you double-click the PC legend on the right, you will enter an individual display mode where you can add an additional group of data point by single-click.

2 Prior information associated to your dataset

2.1 MeSH terms in wordcloud

## [1] "MeSH terms related to RAV725"

## [1] "MeSH terms related to RAV884"

2.2 GSEA

The complete result is saved as {input_name}_genesets_RAV*.csv.

## $`Enriched gene sets for RAV725`
##                                                   Description       NES pvalue
## DMAP_ERY3                                           DMAP_ERY3 -1.622639  1e-10
## DMAP_ERY4                                           DMAP_ERY4 -1.613924  1e-10
## DMAP_ERY5                                           DMAP_ERY5 -1.607208  1e-10
## KEGG_ALZHEIMERS_DISEASE               KEGG_ALZHEIMERS_DISEASE -1.881862  1e-10
## KEGG_HUNTINGTONS_DISEASE             KEGG_HUNTINGTONS_DISEASE -1.999761  1e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION KEGG_OXIDATIVE_PHOSPHORYLATION -2.300524  1e-10
##                                     qvalues
## DMAP_ERY3                      3.251166e-10
## DMAP_ERY4                      3.251166e-10
## DMAP_ERY5                      3.251166e-10
## KEGG_ALZHEIMERS_DISEASE        3.251166e-10
## KEGG_HUNTINGTONS_DISEASE       3.251166e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION 3.251166e-10
## 
## $`Enriched gene sets for RAV884`
##                                                   Description       NES pvalue
## DMAP_ERY3                                           DMAP_ERY3 -1.432912  1e-10
## KEGG_ALZHEIMERS_DISEASE               KEGG_ALZHEIMERS_DISEASE -1.621256  1e-10
## KEGG_CELL_CYCLE                               KEGG_CELL_CYCLE -1.651643  1e-10
## KEGG_HUNTINGTONS_DISEASE             KEGG_HUNTINGTONS_DISEASE -1.644625  1e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION KEGG_OXIDATIVE_PHOSPHORYLATION -1.809433  1e-10
## KEGG_PARKINSONS_DISEASE               KEGG_PARKINSONS_DISEASE -1.840273  1e-10
##                                     qvalues
## DMAP_ERY3                      6.025408e-10
## KEGG_ALZHEIMERS_DISEASE        6.025408e-10
## KEGG_CELL_CYCLE                6.025408e-10
## KEGG_HUNTINGTONS_DISEASE       6.025408e-10
## KEGG_OXIDATIVE_PHOSPHORYLATION 6.025408e-10
## KEGG_PARKINSONS_DISEASE        6.025408e-10

2.3 Publication

The complete result is saved as {input_name}_literatures_RAV*.csv.

## $`Studies related to RAV725`
##      studyName
## 534  ERP114425
## 1030 SRP028155
## 1643 SRP049599
## 2691 SRP071854
## 2705 SRP072038
## 2755 SRP072875
##                                                                                                                                                        title
## 534                                       Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters.
## 1030                                                                                            Transcriptomic analysis of ERR alpha orphan nuclear receptor
## 1643                                                                                            JunB control of keratinocyte-mediated inflammation [RNA-seq]
## 2691                       Parental allele specific single-cell transcriptome dynamics reveal incomplete epigenetic reprogramming in human female germ cells
## 2705 Digitalis-like compounds facilitate redifferentiation of non-medullary thyroid cancer through intracellular Ca2+, cFOS and autophagy dependent pathways
## 2755                                     Single-nucleus RNA-seq on undifferentiated human KD3 myoblasts and differentiated myotubes and mononucleated cells.
## 
## $`Studies related to RAV884`
##      studyName
## 806  SRP015640
## 4066 SRP106788
## 4587 SRP119800
## 4990 SRP132313
## 5393 SRP149535
## 5664 SRP156532
##                                                                                                        title
## 806           Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples
## 4066                                     A practical solution for preserving single cells for RNA sequencing
## 4587                                 Gene expressions in nucleus and cytoplasm at the single cell resolution
## 4990                          Multi-platform single cell transcriptomic profiling as a benchmarking resource
## 5393    Human lineage tracing enabled by mitochondrial mutations and single cell genomics [TF1_clones_scRNA]
## 5664 Human lineage tracing enabled by mitochondrial mutations and single cell genomics [TF1_barcoding_scRNA]